Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
dense
Generated from Trainer
dataset_size:7684
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use shubhamggaur/MedVisionRouter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use shubhamggaur/MedVisionRouter with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("shubhamggaur/MedVisionRouter") sentences = [ "Are there mitotic figures visible?", "skin biopsy showing inflammatory or neoplastic process", "immunohistochemistry staining for cancer subtyping", "skin surface showing pigmented lesion characteristics" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle